Char-RNN

Character-Level RNN Language Model

To train, create some corpus in input.txt, and run ./char-rnn.py train. Some corpus to play with: http://cs.stanford.edu/people/karpathy/char-rnn/

To genearate text, see ./char-rnn.py sample --help for the options.

Generate linux kernel source code:

static int via_playback_set_interrupt(int action, void *data, int val)
{
	struct fpga_type *val_control = ah->ctrl_reg;
	u32 h, copy_ex, val, NULL, { inheri;	/* base of one irda->naturee */
	u32 argv, lva;			/* pointer to the root before ISAN threshold has secondary */
	u8	version = sample_get_array(ir, agent);	/* hv0 AG record much */
	unsigned long			$avp;	\
	write_insert(page + (v & (unsigned long)__xad_serial_page(p) | __PAGE_ACCESSICY_HEADER__());
	__ATTR(bp, 1);
	__put_user(buff, pg_free);*/
	DE_PAGE(mpt, ptr, ipvecs, i).
		(__GFP_IGNORE_IPSEC_NO(PEED_HDR(sp)))
		__pdesc->f_ofname[0].buf->ptr[(__UA_MINSN_CACHE_SIZE].icwsr);

	extent.diff		= bufsize;
	if (hash.b_format->fs_is_data.uscq.fd_type == 1)
		___asm__ __vparams_allocate(avp_event)
		__PAGE_SIZE	+
		up_va(iov, p,
			      p);

		lead = image;
		union agp.hfc * iov_offset =
		    (path->iv_max_position == map.node_size);
		_avg = cause_offset;
    } *va;
	__u8			   reference;
	__get_user_exit(f_rio_type);
}
static void decode_free(unsigned long aregs)
{
	return invalidated_in_sync(fs_info->remote_data, i);
}

void init_vm_voltage_one(struct iagenga_ops *omap2_version,
	irq_set_value_cachep(indio_dev, 0, 0, "%s]")
	 : "_enable is enabled as enabled on !errcomvs output width"
	 * is the compressable.
	 * Archite inserted in the exception from the more than is a POWER_UNIRQ1_IMM_READ if a
	 * provides precision at hope if any packet
	 *	Word.  All given key start of the subtract to using the name.
	 *  Now restore it later in attribute files.
	 */
	u8 num_irq;		/* aligned */
	ap_present = obj_stray;
	acb->owner = parent;
	rw29.phy_flag(port, UDATA306, vid, irq,
			V4L2_CID_BURST, ioc->name);
	else if (pins->irq_ops.intel_stall_min) {
		/* record the address off */
		ret = ath79_ap_ready_wfire_bits(
			ERRORS \
			"isp110x->having/nlan in, position",
			cpu);

	case 0:
	case 3:
	case 4:
		board = capsblrmin(timeout); box_x0 = 6;/* 1^2 vmu, number versalise */
		v = pvt->pages_index;

		ring->un.formats[i] = event->value;
		input_assert_rate(udc);
	} else if (bp_tested(chip)) {
		tmp1 = ACPI_ASUS_REG_TX1;
		/*
		 * LS pending packets, 4==+1 pad word < 0 VBE is enabled 1
		 * it's added and this will have any device.  an evfn allocators after the
		 * functions already read the kernel.
		 */
		if (up)
			break;
	}

	sg_u_state(report);
	err = -ENOTSUPP == IEEE80211_HT_PASSIVE_IDLE;

	if (!*apa_header)
		set_hard_updates(padapter, 0, -1);

	if (!keymap_lookup)
		return PTR_ERR(status);

	if (!urb)
		goto fastpath;
	if (offload_mapped(data)) {
		mappine = security_make_key(dev, size, var_data->end);
		udelay((u8 *)out, sizeof(nesadapter->membase));
	}

	dev_err(dev->dev, "i2c reset, uart_enabled from %d\n", pdev);
	icounture_htc_get_reg(i2c, port);
}

Generate paper: (trained on my personal folder of deep learning papers, converted to a messy pure-text format by pdftotext).

sample shows the network is the convex optimization, second to construct an object. the second task
is not use the expensive experimental responses to originally bit can be replaced by a prior model
and many iterations over tt-fc(u15) exactly on imagenet (cell are obviously as; our approach, tomas
use is made or unrows a challenge on grow use advantage in our variant and gradient recognition at
the performance gradient when there is an input, as well as the calling operation at label averaging
or larger class. in particular for intel-dependent annotation, use of the cifar-10 we prior way to
estimate the loss function. we also work that max-pooling on computation. for example, can be done
shown by r0 . the entire way for each entry phase in convolutional layers until thin, updates , but
output direction to max of the jacobian. then the gradient vectors with negating: recurrent neural
networks has random ordering (piteria. neuropenous words), in oise, svhn). there is clearly solved.
for example, ya& generally [banau, khoderrell, potentially, from recurrent keypoints. brnn tape and
weston, improving neural grammatical model flow images is allows belief networks. neural
generating neural networks, there is not the initial particular marked pseudo-cameral rnns
sophett, pattern wlth designs for faster than the inference in deep learning. in nips (most),

See blog of Andrej Karpathy for more interesting stories on this topic.